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In this research, we investigate a supply chain consisting of a downstream firm who purchases a component from an upstream firm and then transforms it into a final product. The downstream firm has a production capacity constraint and considers to raise capital from an investor through equity financing. The raised capital not only enables the downstream firm to expand the capacity but also allows the investor to hold equity shares in the downstream firm. We derive the optimal pricing and production decisions of the two firms and discuss the optimal equity financing strategy of the downstream firm. We consider two distinct models: the external equity financing model, where the capital is raised from an outside institution, and the internal equity financing model, where the capital is raised from the upstream firm. We show that because the cooperative relationship between the two firms can be improved in the internal equity financing model, the production quantity in this model may be even higher than that in a benchmark model with no capacity constraint and no equity holding by the investor in the downstream firm. In addition, the original shareholder of the downstream firm gets more benefit from the internal equity financing activity than from the external equity financing activity. We also analyze the impacts of the key model parameters on the equity financing strategy and find that the dependence of the financing strategy on the initial asset of the downstream firm is quite different in the two models. Moreover, when the production cost of the downstream firm is changed, less capital raised for expanding capacity may create more value for the original shareholder of the downstream firm in each model. Finally, we show that the key finding remains unchanged when deterministic demand is changed to stochastic demand. [ABSTRACT FROM AUTHOR]

We explore the impact of judgement bias on demand forecast accuracy and profit to identify the driving force of decision-makers to be biased. We study the accuracy-maximising forecasts and the profit-maximising forecasts, which results in the least error of demand forecasts and minimum deviation from the optimal order decision, respectively. Under the assumption that period-to-period demand is independent over time, we find that both types of forecasts are biased. It implies that a newsvendor has the motivation to be biased to obtain either a more accurate demand forecast or a higher profit. Moreover, the decision error under the accuracy-maximising forecasts can be lower than that under unbiased demand forecasts and be bounded by twice of the error under profit-maximising forecasts. It suggests that the biased accuracy-maximising forecasts can perform satisfactorily in both forecasts and decisions. We further relax the assumption of independent demand by considering correlated and trended demand processes, and show the robustness of the positive impact of judgement bias. We then propose a method to solve pure data-driven newsvendor problem and examine its performance with empirical evidence. Our paper contributes to the literature on behavioural operations management by investigating the rationality of judgement bias and its implications. [ABSTRACT FROM AUTHOR]

This paper explores the use of initial coin offerings (ICOs) as a means of fundraising for companies through the issuance of blockchain‐based tokens. Specifically, we investigate how a company can implement ICOs and issue tokens supported by its products while taking into account the presence of network effects. We examine several aspects of ICOs, including the impacts of network effect on the company's optimal token sales design, such as the optimal token issuance price, the ICO cap (which refers to the number of tokens to be issued), and the amount of funds raised. We also consider the cases of deterministic and uncertain network effects, the impacts of limited speculators purchasing tokens during the ICO period, the comparison between ICOs and traditional bank financing, and the impact of ICO cost. The research unfolds in five key parts. Firstly, leveraging a baseline model, we derive equilibrium outcomes and the optimal token sales design, discerning the impact of deterministic and uncertain network effects. Notably, network effect uncertainty consistently benefits companies, ensuring profit resilience despite potential challenges, although in the face of such uncertainty, the ICO company may decrease the raised fund. Secondly, we investigate the consequences of a limited number of speculators in the ICO landscape, finding that adjustments on the token sales design are not required under low expected network effect. Thirdly, a comparative study between ICOs and bank financing reveals distinctive advantages of ICOs. ICOs prove capable of decreasing profit volatility while maintaining an equivalent profit compared to traditional bank financing. This insight offers valuable guidance for companies seeking optimal financing methods aligned with their risk preferences. Lastly, the impact of ICO cost on outcomes and token sales design is scrutinized. Contrary to expectations, ICO cost does not uniformly lead to an increase in the ICO cap, underscoring the nuanced relationship between costs and fundraising strategies. [ABSTRACT FROM AUTHOR]

Immunotherapy has brought significant advancements in the treatment of lung adenocarcinoma (LUAD), but identifying suitable candidates remains challenging. In this study, we investigated tumour cell heterogeneity using extensive single‐cell data and explored the impact of different tumour cell cluster abundances on immunotherapy in the POPLAR and OAK immunotherapy cohorts. Notably, we found a significant correlation between CKS1B+ tumour cell abundance and treatment response, as well as stemness potential. Leveraging marker genes from the CKS1B+ tumour cell cluster, we employed machine learning algorithms to establish a prognostic and immunotherapeutic signature (PIS) for LUAD. In multiple cohorts, PIS outperformed 144 previously published signatures in predicting LUAD prognosis. Importantly, PIS reliably predicted genomic alterations, chemotherapy sensitivity and immunotherapy responses. Immunohistochemistry validated lower expression of immune markers in the low‐PIS group, while in vitro experiments underscored the role of the key gene PSMB7 in LUAD progression. In conclusion, PIS represents a novel biomarker facilitating the selection of suitable LUAD patients for immunotherapy, ultimately improving prognosis and guiding clinical decisions. [ABSTRACT FROM AUTHOR]

Fraudulent returns have become increasingly frequent in the development of online retail platforms, leading to huge losses for retailers. A blockchain‐based system can help eliminate this problem primarily because the data stored in a blockchain network cannot be modified. In this paper, we study the impact of blockchain technology that aims to eliminate fraudulent return behavior on retailers and their upstream suppliers. We develop a game‐theoretic model that comprises a supplier, a retailer, and customers and analyze the equilibrium outcomes. The results show that the retailer would (not) adopt blockchain technology if the efficiency of the supplier's investment in the innovation of its products is high (low). Moreover, if the retailer's loss from the return is low (high), the adoption of blockchain technology decreases (increases) the supplier's investment and the wholesale and retail prices. In terms of its effect on profitability, we reveal that if the fraudulent return losses are high, using blockchain technology always benefits the supplier; otherwise, it increases retailer profitability and supply chain performance but may make the supplier suffer losses in profits. Interestingly, the retailer would adopt blockchain technology to ensure profitability in situations where the number of customers without fraudulent returns and the intensity of losses are low; however, this weakens supplier profitability and meanwhile reduces supply chain performance. [ABSTRACT FROM AUTHOR]

OBJECTIVES The goal was to explore clinical factors and build a predictive model for the disease-free and overall survival of patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant chemotherapy combined with immune checkpoint inhibitors. METHODS Inclusion criteria for patients in this multicentre study were as follows: (i) Patients who were diagnosed with stages I–III NSCLC after a bronchoscopy biopsy or puncture; (ii) patients who were examined with computed tomography/positron emission tomography-computed tomography before treatment and surgery; (iii) patients who received neoadjuvant chemotherapy combined with immune checkpoint inhibitors for 2 to 6 cycles preoperatively; (iv) patients whose peripheral blood indicators and tumour markers were assessed before treatment and preoperatively; (v) patients who underwent radical lung cancer surgery after neoadjuvant therapy. Cases were divided into high- and low-risk groups according to 78 clinical indicators based on a 10-fold Least Absolute Shrinkage and Selection Operator selection. We used Cox proportional hazards models to predict disease-free and overall survival. Then, we used time-dependent area under the curve and decision curve analyses to examine the accuracy of the results. RESULTS Data were collected continuously, and 212 and 85 cases were randomly assigned to training and testing sets, respectively. The area under the curve for the prediction of disease-free survival (training: 1 year, 0.83; 2 years, 0.81; 3 years, 0.83 versus testing: 1 year, 0.65; 2 years, 0.66; 3 years, 0.70), overall survival (training: 1 year, 0.86; 2 years, 0.85; 3 years, 0.86 versus testing: 1 year, 0.66; 2 years, 0.57; 3 years, 0.70) were determined. The coefficient factors including pathological response; preoperative tumour maximum diameter; preoperative lymph shorter diameter; preoperative tumour and lymph maximum standardized uptake value; change in tumour standardized uptake value preoperatively; and blood-related risk factors were favourably associated with prognosis (P  < 0.001). CONCLUSIONS Our prediction model, which integrated data from preoperative positron emission tomography-CT, preoperative blood parameters and pathological response, was able to make highly accurate predictions for disease-free and overall survival in patients with NSCLC receiving neoadjuvant immunity with chemical therapy. [ABSTRACT FROM AUTHOR]

[Objectives] To improve corrosion resistance of 2024 aluminum alloy,this paper investigates the preparation of an advanced coating and its corrosion behaviors.[Methods] 2024 aluminum alloy was coated with an Al-based amorphous alloy coating using high-velocity air fuel spraying(HVAF)technology at high spraying speed. The composition,microstructure and corrosion morphology of the coating and matrix were analyzed using an X-Ray diffractometer,transmission electron microscope and scanning electron microscope,and potentiodynamic polarization plots and EIS curves in 3.5% NaCl solution were measured.[Results] The results indicate that the cooling rate of the melt particles is higher than the critical cooling rate of the amorphous alloy due to the high spraying speed,which greatly increases the amorphous content of the coating. Meanwhile,the high spraying speed effectively reduces the porosity of the coating. The amorphous content of the coating reached 81.3% and the porosity of the coating was low at 0.35%. Moreover,the corrosion resistance of the Al-based amorphous alloy coating was obviously superior to that of the 2024 aluminum alloy with a lower corrosion current density(8×10-6 A/cm2)and a higher pitting potential(-0.30 V). The impedance value of the coating was four times that of the 2024 aluminum alloy substrate. The corrosion mechanism of the Al-based amorphous alloy coating was homogeneous corrosion,while that of the 2024 aluminum alloy was pitting.[Conclusions] The high amorphous content and low porosity of the Al-based amorphous alloy coating contribute to its improved corrosion resistance.